Raman spectroscopy for minerals identification

ABSTRACT

A method for identifying minerals and other materials illuminates a mineral with monochromatic light for an illumination duration and collects scattered light using a Raman spectrometer detector and an aggregated or average Raman spectrum data is determined. True Raman spectrum data is determined by subtracting a blank spectrum. The true Raman spectrum data is compared to reference spectrums to identify the mineral or material. A display or output of one or more of: (a) a name and/or chemical composition of one or more identified minerals; (b) the true Raman spectrum data; and (c) the one or more reference spectrums; are provided. The monochromatic light preferably has a wavelength in the range of about 400 nm to about 425 nm and the Raman spectrometer detector is adapted to detect a Raman-shift range of about 100 cm −1  to about 1400 cm −1 .

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation-in-part of, and claimspriority to, U.S. patent application Ser. No. 16/108,474 filed on Aug.22, 2018, the contents of which are incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to the field of Raman Spectroscopy, andmore particularly, to apparatus and software for identifying unknownminerals. While the invention is designed to be optimized for mineralidentification, some components are anticipated to be advantageous formany other applications of Raman spectroscopy as well.

BACKGROUND OF THE INVENTION

Mineral identification is essential to most geoscience investigationsincluding research (field and experimental), industrial, and regulatoryinvestigations in the fields of mineralogy, petrology, geochemistry,geochronology, petroleum exploration, mineral exploration, mininggeology, gemology, mineral processing, building materials, environmentalhealth, medical mineralogy, forensic mineralogy, planetary geology, andmore. Meeting the needs of all of these industries and applicationsrequires analytical technology that is dependable, accurate, capable ofhandling a wide range of physical requirements (sample size, sampleformat, sample location), accessible to users having a wide variety oftraining, and, ideally, affordable.

Routine mineral identification (“RMI”) can be defined as an analyticalprocess that provides for identification of a mineral sample with a lowlevel of sample and/or instrument preparation for each analysis, withrelatively quick results, and with a high level of dependability(repeatability and probability of accurate results). However, when ananalytical process is highly expensive, requires high amounts ofphysical space and/or physical support, requires high levels ofmaintenance and calibration, and/or requires a high level of educationand training to operate, it is also impractical—it cannot be widelydeployed to or readily available to a high percentage of thoseindividuals/institutions who routinely need analytical results. As suchpractical RMI (“PRMI”) can be defined as an RMI system that requiresmoderate to low amounts of user training, cost, space, and maintenance.

While individuals with sufficient training and experience may be able toidentify many minerals with a few hand tools, there is a widespread needfor more analytical and automated means of routine mineralidentification. The next level of technology traditionally employed ispolarized light microscopy, however, this requires considerable samplepreparation and continues to require considerable training andexperience. Since the middle of the 20th century the primarytechnological tool employed for RMI has been powder X-ray diffraction(“PXRD”). Modern improvements have reduced the physical size and cost ofPXRD instrumentation and computers have provided for considerableautomation of the analytical process, but its application generallyrequires extraction and powdering of the mineral sample. This type andamount of sample preparation means that PXRD requires asample-preparation delay before each analysis and is extremelydifficult-to-impossible to apply to small mineral grains or mineralgrains in situ (without extraction). Additionally, despite the reductionin instrument size that has been achieved for PXRD, it continues to beimpractical to attempt to employ PXRD out into a field investigationenvironment.

In contrast to PXRD, Raman spectroscopy can be employed with little tono sample preparation, in situ, and on physical scales down to themicron level. Raman instruments also typically require relatively littlespace and both field portable and hand-held units are available.However, a general lack of optimization of the Raman technology forapplication to minerals (and man-made crystalline solids as well) hasprevented widespread adoption of Raman technology for RMI.

The Raman Effect produces a change (shift) in the wavelength of lightscattered by a substance. This shift is the result of light interactingwith the quantized energy levels of molecular vibrations—as such themagnitude of this wavelength shift and the intensity of Raman scatteringare directly related to the mass of the atoms, the nature of bondingforces between neighboring atoms, and the geometric (symmetry)relationships between neighboring bonds. In other words, the spectralposition and intensity of the peaks in Raman spectroscopy result fromthe structure and chemistry of the subject substance. Unlike many otherspectroscopy techniques, however, the response from the atoms/moleculesis not at specific characteristic wavelength but is rather at a specificshift (spectral distance) from the wavelength of the excitationsource—generally a laser. As a result Raman instruments using excitationlasers at different wavelengths will produce the same pattern of Ramanshifts from the same substance.

Some additional aspects of the interaction of light with matter becomeimportant when attempting to achieve dependable results from Ramansystems: (1) The Raman Effect is actually a very low probabilityinteraction—much of the incident light is elastically scattered (withouta change in wavelength). (2) Although the pattern of Raman shifts from aparticular target substance is independent of the wavelength of theexcitation laser, the intensity of the Raman peaks are not. Theprobability and, therefore, the intensity of Raman scattering increasessteadily as the wavelength of the excitation source decreases. (3) Someof the energy of the incident light is absorbed and converted to thermalenergy. Depending on the amount absorbed by any specific targetsubstance and its thermal conductivity and thermal stability, the targetsubstance may be modified or even destroyed by the Raman laser. (4)Last, but far from least, for many substances some of the absorbed lightenergy is re-emitted as longer-wavelength light—an effect referred to asfluorescence or photoluminescence. When a target substance responds tothe Raman laser with fluorescent light its intensity tends to bestronger than Raman scattered light, often orders of magnitude stronger.Unlike the Raman Effect, however, the intensity of fluorescence, evenwhether it occurs at all, does depend upon the wavelength of theexcitation light source.

The basic components of a Raman spectrometer system are as follows: (1)a laser as the excitation light source since the need to measurewavelength shifts and the low probability of the Raman Effect leads to aneed for a high intensity monochromatic excitation source; (2) opticalcomponents to focus the laser beam onto the sample; (3) opticalcomponents to direct the light scattered/emitted from the sample towardsa spectrograph; (4) a long-pass or notch filter positioned between thesample and the spectrograph to separate the wavelength-shifted lightfrom elastically scatted light at the incident wavelength; and (5) aspectrograph typically employing a diffraction grating and a solid-statearray detector.

For standardized sample holders without spatial specificity like liquidsin a cuvette, mechanical sample positioning is sufficient. Whenspatially specific sample positioning is required some kind of opticalpointing system is required—often this is a microscope with the lasersource coaxially introduced with partially reflecting mirrors. Finally,for reasons ranging from physical convenience to a practical need toseparate the spectrometer from the sample, many Raman spectrometersemploy a fiber-optic bundle with the laser source being deliveredthrough a central fiber and scattered light being carried back to thespectrometer through the remaining fibers in the bundle. In general,again due to the weakness of the Raman Effect, the sample must beisolated from any ambient light.

The performance challenges of Raman technology often intersect or evencompete with one another resulting in performance trade-offs. Thecentral performance challenges are the minimum Raman shift, spectralresolution, spectral range, signal sensitivity, and reducing/avoidingfluorescence interference.

The minimum Raman shift is determined by the type and quality of thecutoff filter. This performance parameter is only critical when theRaman spectrum of the materials being studied has important or criticalpeaks in the low Raman shift region (e.g., less than 300 cm⁻¹).

The technology required to increase spectral range generally reducesresolution. This trade-off can be reduced by increasing the physicalsize of the spectrograph and/or increasing the pixel width of the arraydetector.

Signal sensitivity refers to the proportion of the signal response lightfrom the sample that reaches and is recorded by the detector. Again,there are technical trade-offs between sensitivity and other performancefactors. The technology employed to increase resolution often reducessignal throughput. The signal-gathering optics can also be a limitingfactor, especially fiber-optics that capture a very low solid angle oflight from the sample. High signal sensitivity is only critical forsample substances that are weak Raman scatterers.

Many sample substances do not respond to the laser light withfluorescence emission, however, when fluorescence light is emitted fromthe sample it is often so much more intense than the Raman response thatthe Raman spectrum of the sample cannot be observed at all. The moreimportant it is to be able to obtain Raman spectra from a wide varietyof substances, the more critical it is to employ some technical strategyfor reducing the magnitude of fluorescence or the probability offluorescence.

Fluorescence is arguably the greatest challenge to the applicationdependability of Raman spectrometry and many solution have been used tosolve this problem. Following is a brief description of each includingtheir shortcomings when applied to PRMI.

Multiple laser systems: If serious fluorescence-interference exists fora specific target material with a specific Raman laser, onewell-established solution is to configure the Raman spectrometer withmore than one laser. Since fluorescence occurs over a specific range ofwavelengths, one can simply switch to a laser wavelength that does notexcite fluorescence (at least over the spectral range examined by Ramanspectrometry). Since some fluorescent centers produce fluorescence overa broad range of wavelengths and since some minerals contain multiplefluorescent centers, it could easily require more than two laserwavelengths to ensure a high likelihood of being able to obtain a Ramanspectrum for a wide variety of targets (minerals species). Some Ramansystems are configured with 4 or 5 lasers. The limitations of thisapproach for PRMI are expense and complexity. Employing even two lasersrequires much more training and experience to operate and typicallyresults in a Raman system that costs 50% to 90% more than the same modelconfigured with only one laser. Raman systems with 4 and 5 lasersrequire considerable space, cost, and maintenance.

Sequentially shifted excitation: This type of fluorescence-rejectionsolution involves either an adjustable-wavelength laser or adual-wavelength laser. Two Raman spectra are collected using two lasersource wavelengths that are very close together. The two spectra areprocessed with the assumption that Raman features occur in the sameposition in each spectrum and fluorescence features shift an amountequal to the difference between the source wavelengths. This approach isonly slightly less expensive than a multiple-laser system since itavoids the duplication of lasers and other spectrometer components. Thelimitations of this approach for PRMI are spectral artifacts and breadthof application. While analysis time (the time to collect two spectrainstead of one) is also a factor, some systems can collect the twospectra concurrently. Shifted excitation systems are known to producesome non-Raman spectral artifacts out of the spectrum-processingemployed. In addition, the greatest limitation is probably the fact thatshifted-excitation is ineffective for samples producing fluorescenceorders of magnitude brighter than Raman intensities. In these cases theRaman peaks have similar intensity (or less) than electronic noise andthe “shot noise” typical of photon counting and cannot be extracted fromthe dual spectra.

Gated Raman: Gated Raman takes advantage of the difference in time scalein which Raman scattering and fluorescence occur. Employing a high-speedpulsed laser and a fast gating detector, such a Raman system iscarefully timed so that the detector is open when the laser pulse hitsthe sample and closes within a few hundred ps before most or sometimesany fluorescence light can be generated. The laser and detector arecontrolled to wait until any fluorescence is likely to have decayed andthen repeat this excitation/detection pattern as many times aspossible—thousands of times per second. The limitations of this approachfor PRMI are expense and sensitivity. Until recently all gated-Ramansystems were expensive and complex custom-made optical-bench systems.Even the commercial gated Raman system that is now available costs threetimes as much as non-gated system of otherwise-equivalentsophistication. Additionally, an important implication of the mode ofoperation of a gated system is that, even if it is operated at a pulsefrequency of 100 kHz, it spends only 5% of every second with thedetector open and collecting data. Such a system may have to collectdata for an impractical period of time for the many minerals that areweak Raman scatterers.

IR Lasers: Many Raman instrument makers offer systems with an IR lasersource (e.g., 1064 nm) because there is little likelihood that such along wavelength source will excite fluorescence. The primary limitationof this approach for PRMI is sensitivity. As stated above, theprobability of Raman scattering increases as the wavelength of theexcitation source decreases. In fact, Raman intensity increases with thewavelength to the fourth power. This means as the source wavelength getslonger Raman intensities from the same subject drop quickly. Since manyminerals are weak Raman scatterers, the IR laser Raman system haslimited application to minerals as a whole.

UV lasers: Raman systems employing UV lasers are relatively rare due totheir spectral performance challenges and expense. A UV Raman system hasto examine a narrow spectral region, making it technologically difficultto achieve adequate spectral resolution and the minimum Raman shift ofsuch systems is typically 350 to 450 nm. The limitations of UV Raman forPRMI are expense and performance. The components required to meet theperformance challenges in the UV are expensive and many, many mineralshave important Raman peaks at Raman shifts below 400 nm.

Anti-Stokes Raman: In detail, the Raman Effect encompasses wavelengthshifts to longer wavelengths (Stokes Raman) and wavelength shifts toshorter wavelengths (Anti-Stokes Raman). Since, in theory, fluorescenceproduces light at longer wavelengths than the excitation source, it hasbeen proposed that fluorescence-interference can be avoided byconfiguring a Raman spectrometer to detect Anti-Stokes Raman scattering.The limitations of Anti-Stokes Raman include shorter wavelengthfluorescence and Raman intensity limitations. While it is true thatnarrow-range fluorescence from inner-shell electrons does not occur atwavelengths longer then the excitation source, it has been found thatbroad-range fluorescence resulting from excited outer-shell electronsextends significantly into wavelengths shorter than the excitationsource due to the complex interactions between these electrons throughtheir relaxation pathways. Additionally, Anti-Stokes Raman depends onsome of the quantum vibrational states within the target sample beingalready at an energy level above their ground state when excited byphotons from the Raman laser. As a result, Anti-Stokes Raman intensitiestend to be weaker than Stokes Raman and Anti-Stokes Raman intensitiessteadily decrease as the Raman-shift of peaks (from the same sample)increases.

Furthermore, it has become very popular to produce hand-held analyzersfor use in field applications. However, this becomes physically andtechnologically impractical for Raman systems using a shorter wavelengthlaser source partially due to the physical size of the spectrographcomponent required to achieve useful spectral resolution. One challengefor a portable/field Raman system is to provide for laser lightdelivery, signal light collection, and sample visibility for aimingthrough a single optical port. Many hand-held units and some portablesdo not even try to provide for optical analysis aiming and depend uponthe analysis site being large enough to simply point the optical frontend of the analyzer at it. Additionally, a Raman analyzer for PRMI mustbe efficient at signal light collection to work successfully with weakRaman scatterers and field geologic applications often have a need toanalyze mm size mineral grains.

Accordingly, there is a need for a more dependable, efficient, andcost-effective Raman Spectroscopy system for PRMI that avoids thefluorescence-interference problem. Additionally, it is desired that aportable PRMI system be available for in-field or in-house applications.It is also desired that software be available to efficiently assist thePRMI Raman Spectroscopy system with Raman spectrum data-collection,processing, noise-removal, and identification of unknown minerals.

SUMMARY OF THE INVENTION

Accordingly, embodiments of the present invention include a method ofidentifying materials, and particularly, minerals, and software forRaman spectrum data-collection, processing, and identification ofunknown minerals.

In one embodiment of the present invention, a method for identifyingminerals, comprises the steps of illuminating a sample holder areahaving no sample therein for a blank illumination duration withmonochromatic light having a wavelength; collecting a blank scatteredlight resulting from the blank illumination duration using at least oneRaman spectrometer detector; executing software in a digital computer todetermine a blank spectrum data corresponding to the blank scatteredlight; illuminating a mineral sample in the sample holder area for afirst illumination duration with the same monochromatic light andcollecting a first scattered light resulting from the first illuminationduration using the at least one Raman spectrometer detector; executingsoftware in a digital computer to determine a first Raman spectrum datacorresponding to the first scattered light; and executing software in adigital computer to determine a true Raman spectrum data determined by adifference between the first Raman spectrum data and the blank spectrumdata. Desirably, the method includes additional steps of illuminatingthe mineral sample for a second or more illumination duration(s) withthe same monochromatic light and collecting second or more scatteredlight resulting from the second illumination duration(s) using the atleast one Raman spectrometer detector; executing software in a digitalcomputer to determine second or more Raman spectrum data correspondingto the second or more scattered light; executing software in a digitalcomputer to determine an aggregated Raman spectrum data corresponding tothe first scattered light and the second or more scattered light; andexecuting software in a digital computer to determine the true Ramanspectrum data determined by a difference between the aggregated Ramanspectrum data and the blank spectrum data. Preferably, the monochromaticlight has a wavelength in the range of about 400 nm to about 425 nm.Preferably the Raman spectrometer detector is adapted to detect aRaman-shift range of about 100 cm⁻¹ to about 1400 cm⁻¹.

The true Raman spectrum data is compared to one or more referencespectrums to determine if the true Raman spectrum data corresponds toone or more reference spectrums by a software process executed on adigital computer. Preferably, the reference spectrums comprisemathematical models of Raman spectrums of different minerals and/ornoise-free reference spectrum data sets generated by mathematical modelsof Raman spectrums of minerals.

One or more of: (a) a name and/or chemical composition of one or moreminerals identified by the one or more reference spectrums correspondingto the true Raman spectrum data; (b) the true Raman spectrum data; and(c) the one or more reference spectrums corresponding to the true Ramanspectrum data are output by display on a screen, printout, and/or savingto a data file when reference spectrum(s) corresponding to the trueRaman spectrum data are identified.

The determination of whether the one or more reference spectrumscorresponding to the true Raman spectrum data is preferably provided bycalculating an identification score for each reference spectrum relativeto the true Raman spectrum data using a formula that includes both acoincident-peak term and a missing-peak-penalty term. In one embodiment,the identification score is determined by the formula:IDscore=sum((a _(i) +b _(i))/2−V*abs(a _(i) −b _(i))*(1−min(a _(i) ,b_(i))){circumflex over ( )}U),wherein a_(i) and b_(i) are the intensities of the true first spectrumdata and the reference spectrum, respectively, at the i^(th) value ofRaman shift, and V and U are user-selectable parameters.

The calculated identification scores can be sorted in descending order;and an output may be presented that includes one or more of: (a) a nameand/or chemical composition of one or more minerals identified by one ormore reference spectrums corresponding to the true Raman spectrum data;(b) the true Raman spectrum data; (c) the one or more referencespectrums corresponding to the true Raman spectrum data; and (d) thecalculated identification score of the one or more reference spectrumscorresponding to the true Raman spectrum data.

The method desirably also provides that if collected spectrum dataindicates saturation of a detector used to receive the scattered light,then that spectrum data is not used in calculating the true Ramanspectrum data, and preferably, the method automatically runs anadditional test cycle where the time period of the additionalillumination duration is less than the illumination durations thatcaused saturation in the detector.

Similarly, if a minimum if a signal-to-noise ratio minimum is not metand the Raman spectrum data does not indicate the presence of at leastone peak, then the method automatically runs an additional test cyclewhere the time period of the additional illumination duration is greaterthan the illumination durations of the prior test cycles.

In one embodiment of the present invention, a computer readable mediumaccessible by at least one processor and at least one Raman spectrometerfor identifying materials is provided. The computer readable mediumincludes software instructions executable by the at least one processorfor: (A) instructing a monochromatic light source to illuminate asample-free area for a first illumination duration with monochromaticlight; (B) receiving a blank spectrum data corresponding to firstscattered light collected by the at least one detector and resultingfrom the first illumination duration; (C) instructing the monochromaticlight source to illuminate a material sample for a second illuminationduration with monochromatic light; (D) receiving a first spectrum datacorresponding to second scattered light from the material samplecollected by the at least one detector and resulting from the secondillumination duration; (E) subtracting the blank spectrum data from thefirst spectrum data, resulting in a true first spectrum data; and (F)comparing the true first spectrum data to one or more referencespectrums to determine a match.

In some embodiments, the comparison of step (F) includes: (i)calculating a identification score for each reference spectrum relativeto the true first spectrum data that includes both a coincident-peakterm and a missing-peak-penalty term such as:IDscore=sum((a_(i)+b_(i))/2−V*abs(a_(i)−b_(i))*(1−min(a_(i),b_(i))){circumflexover ( )}U), wherein a_(i) and b_(i) are the intensities of the truefirst spectrum data and the reference spectrum, respectively, at thei^(th) value of Raman shift, and V and U are user-selectable parameters;(ii) sorting the calculated identification scores in descending order;and (iii) outputting the sorted identification scores for viewing by auser.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (G) analyzing a first signal-to-noise ratioof the true first spectrum data; (H) receiving a user-selectedsignal-to-noise ratio target; and (I) if the signal-to-noise ratio ismet, then: (i) repeating steps C-G to generate a true second spectrumdata and a second signal-to-noise ratio; (ii) comparing the first andsecond signal-to-noise ratios; and (iii) if the second signal-to-noiseratio is less than the first signal-to-noise ratio, alerting the user topossible damage to the material sample.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (J) if the first spectrum data indicatessaturation of a detector used to receive the scattered light, then: (i)instructing the monochromatic light source to illuminate the materialsample for a third illumination duration with monochromatic light,wherein the third illumination duration is less than the secondillumination duration; (ii) receiving a third spectrum datacorresponding to third scattered light from the material samplecollected by the at least one detector and resulting from the thirdillumination duration; (iii) repeating step (E) to determine a truethird spectrum data; and (iv) storing the true third spectrum data.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (K) if the signal-to-noise ratio target isnot met and if the first spectrum data does not indicate the presence ofat least one peak, then: (i) instructing the monochromatic light sourceto illuminate the material sample for a fourth illumination durationwith monochromatic light, wherein the fourth illumination duration ismore than the second illumination duration; (ii) receiving a fourthspectrum data corresponding to fourth scattered light from the materialsample collected by the at least one detector and resulting from thefourth illumination duration; (iii) repeating step (E) to determine atrue fourth spectrum data; and (iv) storing the true fourth spectrumdata; and (L) if the signal-to-noise ratio target is not met and if thefirst spectrum data indicates the presence of at least one peak, then:(i) calculating a fifth illumination duration intended to achieve thesignal-to-noise ratio target; (ii) instructing the monochromatic lightsource to illuminate the material sample for the fifth illuminationduration with monochromatic light; (iii) receiving a fifth spectrum datacorresponding to fifth scattered light from the material samplecollected by the at least one detector and resulting from the fifthillumination duration; (iv) repeating step (E) to determine a true fifthspectrum data; and (v) storing the true fifth spectrum data.

In some embodiments, prior to step (C), the computer readable mediumincludes additional software instructions for receiving a user-selectedmaximum total exposure time. The medium also includes softwareinstruction for limiting a total illumination time based on theuser-selected maximum total exposure time.

In some embodiments, prior to step F, the computer readable mediumincludes additional software instructions for removing noise from theone or more reference spectrums.

In an alternative embodiment of the present invention, a computerreadable medium accessible by at least one processor and at least onestorage is provided. The computer readable medium includes softwareinstructions executable by the at least one processor for: (A)instructing a monochromatic light source to illuminate a material samplefor a first illumination duration with monochromatic light; (B)receiving a first spectrum data corresponding to scattered light fromthe material sample collected by at least one detector and resultingfrom the first illumination duration; and (C) comparing the firstspectrum data to one of more reference spectrums to determine a match,the comparison including: (i) calculating a identification score foreach reference spectrum relative to the true first spectrum data using aformula that includes both a coincident-peak term and amissing-peak-penalty term such as:IDscore=sum((a_(i)+b_(i))/2−V*abs(a_(i)−b_(i))*(1−min(a_(i),b_(i))){circumflexover ( )}U), wherein a_(i) and b_(i) are the intensities of the truefirst spectrum data and the reference spectrum, respectively, at thei^(th) value of Raman shift, and V and U are user-selectable parameters;(ii) sorting the calculated identification scores in descending order;and (iii) outputting the sorted identification scores for viewing by auser.

In some embodiments, prior to step (A), the computer readable mediumincludes additional software instructions for instructing amonochromatic light source to illuminate a sample-free area for a secondillumination duration with monochromatic light, and receiving a blankspectrum data corresponding to second scattered light collected by theat least one detector and resulting from the second illuminationduration.

In some embodiments, prior to step (C), the computer readable mediumincludes additional software instructions for subtracting the blankspectrum data from the first spectrum data, resulting in a true firstspectrum data that is used in the Identification score formula of step(C)(i).

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (D) analyzing a first signal-to-noise ratioof the first spectrum data; (E) receiving a user-selectedsignal-to-noise ratio target; and (F) if the signal-to-noise ratiotarget is met, then: (i) repeating steps (A)-(D) to generate a secondspectrum data and a second signal-to-noise ratio; (ii) comparing thefirst and second signal-to-noise ratios; and (iii) if the secondsignal-to-noise ratio is less than the first signal-to-noise ratio,alerting the user to possible damage to the material sample.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (G) if the first spectrum data indicatessaturation of a detector used to receive the scattered light, then: (i)instructing the monochromatic light source to illuminate the materialsample for a third illumination duration with monochromatic light,wherein the third illumination duration is less than the firstillumination duration; (ii) receiving a third spectrum datacorresponding to third scattered light from the material samplecollected by the at least one detector and resulting from the thirdillumination duration; and (iii) storing the third spectrum data.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (H) if the signal-to-noise ratio is not metand if the first spectrum data does not indicate the presence of atleast one peak, then: (i) instructing the monochromatic light source toilluminate the material sample for a fourth illumination duration withmonochromatic light, wherein the fourth illumination duration is morethan the first illumination duration; (ii) receiving a fourth spectrumdata corresponding to fourth scattered light from the material samplecollected by the at least one detector and resulting from the fourthillumination duration; and (iii) storing the fourth spectrum data; and(I) if the signal-to-noise ratio is not met and if the first spectrumdata indicates the presence of at least one peak, then: (i) calculatinga fifth illumination duration intended to achieve the signal-to-noiseratio target; (ii) instructing the monochromatic light source toilluminate the material sample for the fifth illumination duration withmonochromatic light; (iii) receiving a fifth spectrum data correspondingto fifth scattered light from the material sample collected by the atleast one detector and resulting from the fifth illumination duration;and (iv) storing the fifth spectrum data.

In some embodiments, prior to step (A), the computer readable mediumincludes additional software instructions for receiving a user-selectedmaximum total exposure time. The medium also includes softwareinstruction for limiting a total illumination time based on theuser-selected maximum total exposure time.

In some embodiments, prior to step (C), the computer readable mediumincludes additional software instructions for removing noise from theone or more reference spectrums.

In another embodiment of the present invention, a computer readablemedium accessible by at least one processor and at least one storage isprovided. The computer readable medium includes software instructionsexecutable by the at least one processor for: (A) instructing amonochromatic light source to illuminate a sample-free area for a firstillumination duration with monochromatic light; (B) receiving a blankspectrum data corresponding to first scattered light collected by atleast one detector and resulting from the first illumination duration;(C) instructing the monochromatic light source to illuminate a materialsample for a second illumination duration with monochromatic light; (D)receiving a first spectrum data corresponding to second scattered lightfrom the material sample collected by the at least one detector andresulting from the second illumination duration; (E) subtracting theblank spectrum data from the first spectrum data, resulting in a truefirst spectrum data; and (F) comparing the true first spectrum data toone or more reference spectrums to determine a match, the comparisonincluding: (i) calculating an identification-score for each referencespectrum relative to the true first spectrum data using a formula thatincludes both a coincident-peak term and a missing-peak-penalty termsuch as:IDscore=sum((a_(i)+b_(i))/2−V*abs(a_(i)−b_(i))*(1−min(a_(i),b_(i))){circumflexover ( )}U), wherein a_(i) and b_(i) are the intensities of the truefirst spectrum data and the reference spectrum, respectively, at thei^(th) value of Raman shift, and V and U are user-selectable parameters;(ii) sorting the calculated identification scores in descending order;and (iii) outputting the sorted identification scores for viewing by auser.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (G) analyzing a first signal-to-noise ratioof the true first spectrum data; (H) receiving a user-selectedsignal-to-noise ratio target; and (I) if the signal-to-noise ratiotarget is met, then: (i) repeating steps (C)-(G) to generate a truesecond spectrum data and a second signal-to-noise ratio; (ii) comparingthe first and second signal-to-noise ratios; and (iii) if the secondsignal-to-noise ratio is less than the first signal-to-noise ratio,alerting the user to possible damage to the material sample.

In some embodiments, the computer readable medium includes additionalsoftware instructions for: (J) if the true first spectrum data indicatessaturation of a detector used to receive the scattered light, then: (i)instructing the monochromatic light source to illuminate the materialsample for a third illumination duration with monochromatic light,wherein the third illumination duration is less than the secondillumination duration; (ii) receiving a third spectrum datacorresponding to third scattered light from the material samplecollected by the at least one detector and resulting from the thirdillumination duration; (iii) repeating steps (E)-(G) to determine a truethird spectrum data for use in the Identification score formula of step(F)(i); and (iv) storing the true third spectrum data; (K) if thesignal-to-noise ratio target is not met and if the true first spectrumdata does not indicate the presence of at least one peak, then: (i)instructing the monochromatic light source to illuminate the materialsample for a fourth illumination duration with monochromatic light,wherein the fourth illumination duration is more than the secondillumination duration; (ii) receiving a fourth spectrum datacorresponding to fourth scattered light from the material samplecollected by the at least one detector and resulting from the fourthillumination duration; (iii) repeating steps (E)-(G) to determine a truefourth spectrum data for use in the Identification score formula of step(F)(i); and (iv) storing the true fourth spectrum data; and (L) if thesignal-to-noise ratio is not met and if the true first spectrum dataindicates the presence of at least one peak, then: (i) calculating afifth illumination duration intended to achieve the signal-to-noiseratio target; (ii) instructing the monochromatic light source toilluminate the material sample for the fifth illumination duration withmonochromatic light; (iii) receiving a fifth spectrum data correspondingto fifth scattered light from the material sample collected by the atleast one detector and resulting from the fifth illumination duration;(iv) repeating steps (E)-(G) to determine a true fifth spectrum data foruse in the Identification score formula of step (F)(i); and (v) storingthe true fifth spectrum data.

In some embodiments, prior to step (C), the computer readable mediumincludes additional software instructions for receiving a user-selectedmaximum total exposure time. The medium also includes softwareinstruction for limiting a total illumination time based on theuser-selected maximum total exposure time.

In some embodiments, prior to step (F), the computer readable mediumincludes additional software instructions for removing noise from theone or more reference spectrums.

In yet another embodiment of the present invention, a computer readablemedium accessible by at least one processor and at least one storage isprovided. The computer readable medium includes software instructionsexecutable by the at least one processor for: (A) retrieving, from thestorage, spectrum data; (B) applying a five-point moving averagesmoothing to the spectrum data; (C) normalizing the spectrum data to itsstrongest peak; (D) estimating a root mean square noise of the spectrumdata; (E) adding a baseline model to the spectrum data; (F) performingan iterative loop for: (i) identifying a first potential peak position;(ii) creating a second model at the potential peak position; (iii)fitting the second model to the spectrum data; (iv) calculatingintensity residuals between the spectrum data and the second model; (v)identifying a second potential peak position; (vi) comparing theestimated height of the second potential peak position to a minimumpeak-size limit based on the root mean square noise of the spectrumdata; (vii) if the second potential peak position has an estimatedheight greater than the minimum peak-size limit, repeating steps(i)-(vi); (viii) if the second potential peak position has an estimatedheight less than the minimum peak-size limit, exiting the iterativeloop; (G) subtracting the baseline model from a spectrum model resultingfrom step (F); and (H) outputting the spectrum model to a display.

In some embodiments, the spectrum data is reference spectrum data from adatabase or library of reference spectra.

In some embodiments, step (D) includes additional software instructionsfor: (i) fitting a series of spaced data points to a spline curve over asub-region of the first model; and (ii) calculating the root mean squareof residuals between the data points in the sub-region.

Additional features and details of embodiments of the invention will nowbe described in reference to the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a graph displaying the characteristics of mineralfluorescence.

FIG. 2 is a graph displaying the spectral range of the most importantfluorescence centers and the optimal range of Raman laser sources forPMRI according to an embodiment of the present invention.

FIG. 3 is a diagram of the components of a Raman Spectroscopy systemaccording to an embodiment of the present invention.

FIG. 4 is a diagram of the Raman Spectroscopy system of FIG. 3 withseveral movable components in their respective second positions.

FIG. 5 is a side section view of the objective lens of FIG. 3.

FIG. 6 is a graph displaying the behavior of the Identification Scoreformula of the present invention when used to compare the Raman spectraof Nepheline and Quartz.

FIG. 7a is a graph showing original instrumental data and the individualmodeling functions that constitute the model of the Raman spectrumbefore applying the noise-reduction software of the present invention.

FIG. 7b is a graph showing the final Raman spectrum model resulting fromapplying the noise-reduction software of the present invention to theinstrumental data of FIG. 7 a.

DETAILED DESCRIPTION OF THE INVENTION

Referring now to the drawings, wherein like reference numerals designatecorresponding structures throughout the views. The following examplesare presented to further illustrate and explain the present inventionand should not be taken as limiting in any regard.

FIG. 1 is a chart representing the characteristics of mineralfluorescence. Peaks 22, 24, and 26 are Mn²⁺ fluorescence peaks forwillemite, hardysonite, and rhodonite, respectively. The group ofsharper peaks 32 are Sm²⁺ fluorescence peaks for anhydrite. The dashedcurve 12 is the excitation spectrum for the willemite Mn²⁺ fluorescence22 showing what source wavelengths produce higher or lower intensityfluorescence. The bands of the spectrum 10, 20, and 30 show what portionof the spectrum a Raman spectrometer looks at with a 325 nm, 532, and780 nm laser respectively. Each band represents Raman shifts from 0 to1500 wavenumbers (“cm⁻¹”).

Fluorescence is arguably the greatest challenge to the applicationdependability of Raman spectroscopy. To describe the present solution tothis problem, it is first necessary to review the nature of fluorescenceas well as important distinctions between the Raman Effect andfluorescence.

The Nature of Fluorescence: Fluorescence also begins with absorption ofphoton energy from an incident radiation source (such as a Raman laser),but it results from excitation of electrons to higher energy levels(instead of excitation of inter-atomic vibrational energy levels). Someamount of this absorbed energy is generally transferred to thevibration/rotation of other atoms of the substance (thermal relaxation).In many cases all of the absorbed energy is thermally distributed, butin some cases a photon is emitted carrying the remaining energy(radiative relaxation)—this is fluorescence emission.

Fluorescent Centers: Quite often which materials are and are notfluorescent is predictable since certain elements (e.g., transitionmetals and rare earth elements) are predictably prone to fluorescence,especially in certain valence states and electronic environments (i.e.,crystal structures). Such element/structure combinations formfluorescent centers. Often only minor or trace concentrations of suchelements are required to produce even high intensity fluorescence.

Spectrometry of Fluorescence: It is important to review the spectralwidth, spectral position, and excitation spectrometry of specificfluorescent centers. When the photon-excited electrons are outer shell(valence) electrons (e.g., transition metals), their ground state energy(in a crystalline solid) is highly variable due to the influence ofnearby atoms and their thermal oscillations. The resulting fluorescencehas a specific peak center but has a broad peak shape with FWHM on theorder of 50 to 100 nm, depicted in FIG. 1 as Mn²⁺ fluorescence peaks22-26. When the excited electrons are interior-shell electrons (commonfor rare earth elements) the nearby atomic/electronic influence has muchless effect on ground state energy and the resulting fluorescence ischaracterized by much narrower peaks with FWHM on the order of 5 to 40nm—often a family of narrow peaks from a single fluorescent center, seenin FIG. 1 as Sm²⁺ fluorescence peaks 32. While the position of thefluorescence peak for a specific fluorescent center can vary from onematerial/mineral to another up to 100 nm or more, its position is stillrestricted to a specific region of the optical spectrum. Again,fluorescence from interior shell electrons exhibits less variation inspectral position from one material to another. Before fluorescence canhappen the relevant electrons must absorb photon energy to be excited.While most of the geoscience community thinks of mineral fluorescence asrequiring excitation by a UV light source, the primary requirement is alight source at a shorter wavelength than the resulting fluorescence.For example, a NIR Raman laser at 780 to 785 nm is quite efficient atexciting Nd³⁺ fluorescence centered in the IR (880-1100 nm).Additionally, the probability of absorption/excitation is dependent onlocal electronic structure so that specific wavelengths are more likelyto produce electron excitation than others. FIG. 1 shows the excitationspectrum 12 for the 525 nm Mn²⁺ fluorescence of willemite 22. The curveshows how the intensity of the Mn²⁺ fluorescence as the wavelength ofthe source varies from 300 nm to 520 nm.

Intensity of Fluorescence: It is important to review the relativeintensity of fluorescent light compared to the intensity of Ramanscattering. The intensity of fluorescent light can range from similar toRaman intensities to orders of magnitude higher. The absolute intensityof fluorescence is not even the deciding factor. Since the intensity ofRaman scattering varies orders of magnitude among the various naturalminerals, in some cases a very moderate amount of fluorescence cancompletely overwhelm and mask the Raman spectrum of a mineral. It isrelevant here to note in the example excitation spectrum 12 in FIG. 1that, while most of the features such wavelength-dependence ofexcitation efficiency are going to vary wildly from one mineral toanother, it can be expected that excitation efficiency will always dropdown precipitously as the incident wavelength approaches the centerwavelength of the resulting fluorescence. Also, since fluorescence oftenresults from minor to trace amounts of a fluorescent center, differentsamples of the same mineral species can produce wildly different amounts(and even colors) of fluorescence (including zero).

Range of Optical Fluorescence: There are limits to the range ofwavelength over which optical fluorescence occurs. As the wavelength ofthe source gets longer, into the IR, it becomes less likely that theincident photons will be energetic enough to excite electrontransitions. At the other end of the spectrum, into the UV, it becomesrelevant that the region of the spectrum sampled by a Raman spectrometeris a swath of wavelengths that becomes more narrow as the wavelength ofthe exciting laser becomes shorter, depicted in FIG. 1 as the bands ofthe spectrum 10, 20, and 30 show what portion of the spectrum a Ramanspectrometer looks at with a 325 nm, 532 nm, and 780 nm laserrespectively. While many fluorescent centers are excited by a UV sourcethe wavelength of the resulting fluorescence is rarely in the UV aswell—in the wavelength range of a Raman spectrometer.

Additionally, to optimize Raman spectroscopy for RMI it is important toreview the nature of Raman spectra of minerals and how they differ fromother materials that are typically examined via Raman. The relevantdetails include spectra range, Raman intensities, relative intensity ofRaman peaks, and specimen damage.

Spectra Range: Most Raman instruments offer a Raman-shift range thatgoes up to 3000, 4000, or even 5000 cm⁻¹, many offer a minimumRaman-shift of 200 cm⁻¹; and, as mentioned above, some have minimumRaman-shift limits of 350 to 450 cm⁻¹. Minerals as a group generallyhave most of their Raman peaks below 1200 cm⁻¹ and some minerals (e.g.,sulfides) have most or all of their Raman peaks below 500 cm⁻¹. Whilemany minerals containing light element clusters like H₂O, OH, and CO₃have Raman peaks out beyond 2500 cm⁻¹, inclusion of these peaks is notnecessary for the mineral identification task. Exceptions to thesegeneralizations that are worth noting are graphite and diamond. Diamondstypically have only one Raman peak near 1335 cm⁻¹ and the Raman peaks ofgraphite begin with a strong peak near 1580 cm⁻¹.

Raman Intensities: The range of Raman intensities that can be expectedfrom minerals spans four orders of magnitude. Many important minerals,such as ore minerals, produce rather weak to very weak Ramanintensities.

Relative Intensity of Raman Peaks: While the position of Raman peaks (inRaman-shift) is generally independent of the wavelength of the incidentlaser, the relative intensity of Raman peaks is variable in minerals fortwo reasons. First, since minerals are crystalline, every unit cell or“molecule” in a single mineral grain has the same orientation in space.This leads to variations in relative intensity of Raman peaks inresponse to variations in the spatial/angular relationship between axesof the crystal structure and the directional axis and plane ofpolarization of the Raman laser beam. Second, in common with materialsother than minerals, relative intensity of Raman peaks typically doeschange with the wavelength of the Raman laser.

Specimen Damage: A significant number of minerals, including oreminerals such as sulfides and sulfosalts, are prone to alteration inresponse to heating by the Raman laser. This can take the form ofanything from phase transformations to outright destruction.

Identifying minerals with Raman spectrometry, as with any other exampleof fingerprint-style identification, consists of collecting a clear,characteristic spectrum (free of artifacts not characteristic of thetarget), preparing that spectrum (may include trimming, backgroundremoval, smoothing) and then comparing that spectrum to every entry in adatabase of spectra from known minerals. Some kind of match scoringsystem is used to present the user with a short list of the bestmatches. There are challenges to completing this task at almost everystep.

Clear spectrum: A clear spectrum has sufficient signal strength todistinguish peaks characteristic of the target from noise andbackground. Due to the very large range of Raman intensities that can beexpected from different mineral targets, a Raman user cannot set up astandard set of data collection parameters and apply this repeatedly toa range of different minerals. Additionally, the subset of mineralshaving very weak Raman intensities not only require long counting times,but extra care is required to distinguish their Raman spectra from weakartifacts of the instrument and even the weak signal received from theair above the target. Finally, broad-spectrum fluorescence (e.g., Mn²⁺fluorescence 22-26 in FIG. 1) appears in the narrow spectral view of aRaman spectrometer as high intensity continuum background—at times sointense that Raman peaks are lost. Even when Raman peaks are stilldiscernable above high intensity fluorescence, this fluorescencebackground often does not vary with wavelength smoothly enough for mostbackground-removal algorithms to factor it out.

Characteristic spectrum: In some instances fluorescence light can bepresent in the form of one or more peaks having similar shape andintensity to Raman peaks, particularly narrow (e.g., REE) fluorescencein the range of a longer wavelength Raman system. Additionally, specimendamage often results in both decreasing (including complete quenching)of the Raman peaks characteristic of the (unaltered) mineral andappearance of very different Raman peaks characteristic of what thelaser beam has altered the target mineral into.

Spectrum preparation: Spectrum preparation generally includessubtracting a measured detector background, removal of cosmic rayevents, and subtracting signal background, and may include smoothing.When working with minerals having weak Raman intensities it is common,if not typical, to find that the instrumental output includes spectralfeatures that are not characteristic of the subject mineral, butcharacteristic of the spectrometer and the air and/or other materials inthe path of the spectrometer optics. While such features are weak enoughto be inconsequential when the Raman signal is strong, when the Ramansignal is weak they are problematic and not amenable to standardspectrum preparation methods.

Match scoring: Since Raman spectra of minerals contain peaks of varyingwidth and generally contain overlapping peaks, whole-spectrum matchingvia match-scoring of every spectral channel is generally employed. Dueto the potential for relative Raman intensities from minerals to varywith crystallographic orientation and Raman laser wavelength, aneffective match scoring algorithm for RMI must be relatively insensitiveto relative intensity. Considerable improvement in mineralidentification is achieved simply by square-root squashing (normalizingfollowed by taking the square root of all intensities). However, eventhis squashing technique does not recognize the significance to thematch quality when one spectrum has a strong peak where the comparedspectrum does not.

Reference database: Successful mineral identification via comparisonwith a collection of reference spectra (i.e., Raman spectra of knownminerals) requires that such a collection be both complete and highquality. For PRMI, a truly complete reference database is not criticalsince many hundreds of the over 5000 known mineral species are trulyrare. On the other hand, it is difficult to gather high signal-to-noiseratio (“S/N”) reference spectra on the many weak-Raman minerals. Sincewhole-spectrum matching includes spectrometer intensities in-between theRaman peaks, it has been found that an even modestly noisy (low S/N)reference spectrum with wide spaces between very few peaks can beerroneously matched to a noisy unknown spectrum simply because there wasa high incidence of high points in the noise of the unknown spectrumbeing coordinated by chance with high points in the background noise ofthe reference spectrum. Such problems with noise in the referencespectra only increase when square-root squashing of spectra is employed.The improvement to pattern-match identification offered by squashingonly works if both the unknown spectrum and all of the reference spectraare squashed in the same manner and, while squashing reduces thecontrast between the strongest and weakest peaks in the spectrum, italso inherently magnifies noise amplitude.

The Raman spectroscopy system of the present invention is optimized forPRMI by addressing the challenges described above without becomingprohibitively expensive, bulky, or difficult to operate.

Preferably, the Raman spectroscopy system of the present invention usesa single laser operating within the ideal wavelength range that enablesthe greatest dependability for analysis of minerals while avoiding theexpense and/or complexity of multiple lasers, gated spectrometry, or UVlasers. The ideal portion of the visible light spectrum for PRMI ischosen based upon the intersection of the following factors: (1)Existing data on the fluorescence spectra of minerals must be evaluatedfrom the point of view of where (in the visible spectrum) they do notfluoresce with appropriate attention given to the relationship betweenthe excitation wavelength and the region of fluorescence output. (2)While there exist fluorescence centers in natural minerals that producefluorescent light within every portion of the extended light spectrum(UV through IR), not all fluorescence centers are equally common innatural minerals. (3) The intensity of Raman scattering from the samesample increases strongly as the wavelength of the Raman laserdecreases. Thus, the same intensity of fluorescence is less problematicto collecting a Raman spectrum with shorter wavelength Raman lasers. (4)While the level of technology required, and therefore expense and evensize of the Raman system, becomes steadily greater as the Raman laserwavelength is taken towards and into the UV, there is a distinct breakin the availability of laser cutoff filters capable of producing anacceptable minimum Raman-shift at 400 nm.

The fluorescence spectrometry of many individual minerals has beenstudied and their respective fluorescence centers identified. By far themost common fluorescence center is Mn²⁺ which can produce a fluorescencecenter when substituted into crystallographic sites nominally containingNa²⁺, Mg²⁺, Ca²⁺, Zn²⁺, or Al³⁺. Of lower probability of producing afluorescence center are Fe³⁺ and Cr³⁺, substituting primarily for Al³⁺,and many of the rare earth elements, substituting primarily for Ca. Inaddition to the likelihood that these elements will form fluorescencecenters, a practical evaluation must also consider the crustal abundanceof these elements, which translates directly into the likelihood thatthey will be present in concentrations sufficient to produce significantfluorescence in a wide range of natural examples. FIG. 2 shows the rangeof the extended visible spectrum where fluorescence from Mn, and Fe andCr occur. Additionally, the range of fluorescence from the REE with thehighest crustal abundances, Nd and Ce, are shown. Even though thecrustal abundance of Eu is less than 1/10 that of Nd and Ce, and 1/500that of Mn, the fluorescence range of Eu is shown as well since it tendsto be naturally concentrated in some common minerals like feldspars andfluorite.

Notably, there is, in fact, no region of the spectrum where no mineralsfluoresce. However, a region where mineral fluorescence tends to beuncommon and Raman intensities tend to be stronger is important toachieve the most practical (in size, complexity, and expense) Ramanconfiguration—the single laser system. Therefore, the ideal range forRaman laser sources for PRMI is between the technological transition forcutoff filters—400 nm—and the wavelength where a Raman spectrometer(with a 1500 cm⁻¹ Raman shift range) will begin to capture some of theintense and common Mn fluorescence-425 nm. As depicted in FIG. 2, area40 represents the 400-425 nm range for PRMI lasers, and area 50represents the spectral range of a Raman system using a 425 nm lasersource and viewing Raman shifts up to 1500 cm⁻¹. Testing conducted bythe inventor on a group of 80 mineral species with a 405 nm Raman systemfound only one mineral that produced a problematic level of fluorescence(fluorite). This is a failure rate of 1.25%, while the failure rate of asimilar study with a 780 nm laser was 15%.

FIG. 3 represents a Raman spectroscopy system 100 according to apreferred embodiment of the present invention. Raman system 100 includesa monochromatic light source, such as laser device 110; a sample 114 tobe analyzed; and a spectrograph 210 optimized for light throughput anddetector sensitivity. Preferably, laser device 110 is a stabilized Ramanlaser operating in the 400 nm to 425 nm range including an appropriatecleanup filter to isolate the desired emission line of the laser and adepolarization filter to minimize the impact of crystallographicorientation of target minerals on relative Raman intensities. In someembodiments, laser device 110 is configured for fiber optic delivery ofthe laser beam 113 to the sample 114 through fiber optic cable 112. Inother embodiments, the laser beam 113 is delivered to the sample 114through coaxial laser insertion optics, such as a 70/30 partiallyreflecting mirror.

The sample 114 rests on a holder 116, depicted in FIG. 3 as a flexiblescissor-jack presentation stage. However, any other sample holderdesigned to handle a variety of common geological sample types issufficient. Preferably, a typical system for excluding ambient lightduring data collection is included in the sample-presentation portion ofthe instrument, such as a hard enclosure or a heavy opaque curtain toexclude ambient light from the sample chamber after the sample 114 isloaded onto the holder 116. In some embodiments, the sample chamber alsoincludes at least one visible light source 118, such as a high-outputLED, to illuminate the sample 114 when positioning the sample 114 andaiming the laser beam 113.

In embodiments utilizing fiber optic cable 112, the Raman system 100preferably includes an objective lens 120 having an opening 122 toaccept the end of the fiber optic cable 112, and a focusing lens 124positioned within the opening 122, as depicted in FIG. 5. Preferably,the opening 122 is a hole drilled through the center of the objectivelens 120. In preferred embodiments, the focusing lens 124 positionedwithin the opening 122 after the end of the fiber optic cable 112 suchthat the laser beam 113 passes through the focusing lens 124. Althoughthe drawing figures show the objective lens 120 as a plano-convexobjective lens, any differently shaped lens is sufficient, provided ithas a large diameter and short focal length. The short focal length(high numerical aperture) lens collects a high solid angle ofscattered/signal light 115 from the sample 114 while the fiber opticcable 112 feedthrough provides laser excitation without requiringdelivery through the same optics used for observation and signalcollection.

In preferred embodiments, the signal light 115 passes through objectivelens 120 and is deflected by a primary redirection mirror 130 to aprimary focusing lens 140 designed to focus the signal light 115 onto aspectrograph 210. In some embodiments, the optics are arranged such thatdeflection is unnecessary, and the signal light 115 passes directly fromthe objective lens 120 to the primary focusing lens 140.

In some embodiments, the Raman system 100 includes an assembly ofadditional optics and a video array detector for video image observationand aiming. Preferably, this assembly includes an observationredirection mirror 150, an observation image-formation lens 160, and avideo optical image detector 170. Observation redirection mirror 150 hasa first position 152 in which it is out of the way of the optical pathof the signal light 115, as seen in FIG. 3. Observation redirectionmirror 150 also has a second position 154 in which it is in the opticalpath of the visible light reflected from the sample 114 such that thevisible light is reflected through observation image-formation lens 160into video optical image detector 170, as seen in FIG. 4. Theobservation redirection mirror 150 can be under manual or motor control.Video imaging of the visible light permits confirmation of the analysissite without risk to a user's eyes. Preferably, a micro-switch will turnon the visible light source 118 when the observation redirection mirror150 is in its second position 154, and the switch will turn off thevisible light source 118 when the observation redirection mirror 150 isin its first position 152 for sample analysis.

In preferred embodiments, the Raman system 100 also includes alaser-line blocking filter 180 between the light-collecting optics andthe spectrograph 210. In some embodiments, the blocking filter 180 is anotch filter or a knife-edge long pass filter. A different type ofblocking filter is sufficient, providing it is capable of filtering outthe intense (elastically scattered) laser light while passing the signallight 115 at Raman shifts as low as 150 or, ideally, 100 wavenumbers tothe spectrograph 210 for observation.

Preferably, the Raman system 100 also includes a short-pass filter 190designed to exclude from the spectrograph 210 all light havingwavelengths longer than the laser wavelength (in wavenumbers)+1400wavenumbers. While the laser source wavelength is chosen to avoidexciting fluorescence in the wavelength range being inspected by theRaman system, it may, in many cases, excite strong fluorescence in alonger wavelength portion of the spectrum. Although spectrographs aredesigned to absorb light wavelengths that are not deflected towards thedetector by the grating, when this grating-excluded light includesintense fluorescent light a significant portion of it can defyabsorption and get bounced towards the detector. In preferredembodiments the short-pass filter 190 is movable because short-passfilters always reduce even the desired light wavelengths to some degreeand the minerals having the weakest Raman signal are rarelysignificantly fluorescent. Thus, short-pass filter 190 has a firstposition 192 in which it filters the signal light 115 before thespectrograph 210, and a second position 194 in which it does not filterthe signal light 115. The short-pass filter 190 is movable eithermanually by the user or automatically by a control instrument 220.

Preferably, the optical path between the sample 114 and the spectrograph210 is optimized for maximum light throughput to enable the Raman system100 to work successfully with minerals that are weak Raman scatterers.The spectrograph 210 preferably includes a narrow slit 200 to improvespectral resolution achieved by the spectrograph 210, however, this typeof entrance slit also excludes a large proportion of the scattered lightfrom the sample. In preferred embodiments, the spectrograph slit 200 isa high throughput/high resolution technology such as HTVS(www.tornado-spectral.com).

Preferably, the spectrograph 210 itself is optimized for high throughputand sensitivity and for detection of signal light for a Raman-shiftrange from 100 to 1400 wavenumbers. Additionally, there is particularreason, for PMRI, to configure the spectrograph slit 200 to gather datafrom the 100 to 1400 wavenumber Raman-shift range withoutmoving/scanning the grating. In some embodiments, options for aselectable increase to this range (e.g., through grating scan or secondgrating) are provided for mineralogical research that requires data fromRaman lines at greater Raman shifts.

In preferred embodiments, the Raman system 100 includes electronicand/or optical components designed to manage laser damage to the sample114. This can be accomplished through any means known in the art. Forexample, in some embodiments, this is accomplished by reducing theintensity of the laser beam 113 by controlling the laser device 110itself and/or placing neutral-density filters between the laser device110 and the sample 114. In other embodiments this is accomplished byemploying one or more optical and/or electro-mechanical componentsbetween the laser device 110 and the sample 114 to broaden (defocus) thelaser beam 113 on the sample 114, or to continuously move (raster) thelaser beam 113 in a pattern over an area of the sample 114 that issignificantly larger than the diameter of the laser beam 113.

Preferably, the Raman system 100 also includes a control instrument 220configured to control the Raman system 100 and manage the datacollection process. In preferred embodiments, the control instrument 220is configured to control the intensity of laser beam 113; control anymotor-controlled components, such as observation redirection mirror 150and short-pass filter 190; and collect data, such as spectrograph datafrom the spectrograph 210 and video data from the video optical imagedetector 170. In some embodiments, control instrument 220 alsocommunicates the collected data through a wired or wireless connectionto an external computer capable of executing the software discussedbelow. In other embodiments, control instrument 220 is a computercapable of executing the software discussed below.

In some embodiments, the Raman system 100 is a portable unit and alsoincludes a DC power-input port for powering from an external AC/DCplug-in power supply or an external rechargeable battery pack.

In some embodiments, the Raman system 100 is configured to permitviewing of microscopic material samples. In such embodiments, the opticsdescribed above are microscope optics that preferably provide maximumtransparency for light in the 400-450 nm wavelengths. In preferredembodiments, the laser beam 113 is delivered to the sample 114 throughcoaxial laser insertion optics, such as a 70/30 partially reflectingmirror. In other embodiments, the optics described above are polarizedlight microscopy optics with both transmitted light and reflected lightillumination. Preferably, embodiments of the Raman system configured formicroscopic viewing include a confocal aperture to improve verticalresolution within the sample and enable an important PRMIapplication—the analysis of minerals in thin section under a coverslip.

The external computer/control instrument 220 will preferably have atleast one processor configured to execute data-collection software,spectrum processing and mineral identification software, andnoise-reduction software. The data-collection software is preferablyequipped to work intelligently with minerals that produce very weakRaman signals, with a very wide range of Raman signal strengths, andwith minerals prone to damage by the laser beam. In preferredembodiments, this is accomplished as follows:

First, like any other instrument control computer system, the PRMI datacollection computer is preferably equipped with the communicationsinterface and command set to control and or accept data from alladdressable components such as laser power, laser shutter, motorcontrolled optical components, video imaging, and spectrographcomponents including detector.

Second, to avoid collecting detector background or blank spectra everytime the exposure time is changed, all intensities will preferably beconverted to counts per second.

Third, before beginning data collection on unknown materials, the userwill preferably be prompted to remove any sample and lower the stage (orwhatever sample holding platform is in use) for collection of a blankspectrum that the software will collect with the laser on. Although theexposure time for the blank will be user-adjustable, the default ispreferably a long exposure (e.g., 300 to 600 seconds) so that the resultcharacterizes the features of the optics and the detector rather thanjust electronic noise. While the counts-per-second standard allows forapplication of such a blank to a variety of exposure times on samples,changes to laser power or to any user configurable element of instrumentoptics preferably triggers the software to prompt for a new blank. Auser-configurable “blank lifetime” will tell the software how often toprompt for a new blank to account for minor instrument drift over time.

Fourth, data collection will preferably proceed with an auto-exposurealgorithm in which the software loops through collecting spectra andquickly processing the spectra with peak detection, saturationdetection, and RMS noise estimation routines. This data collection loopbegins at an exposure time fairly unlikely to result in detectorsaturation (e.g., 1 second) and repeats at least once. If the desiredS/N ratio set by the user is met, the loop will exit. If detectorsaturation is detected, the algorithm will drop the exposure time by 50%and repeat. If no peaks were detected, the loop will increase theexposure time by a factor of 10 for the next loop. If peaks weredetected, the software will calculate the exposure time needed toachieve the S/N target and set that exposure time for the next spectrumcollection. Throughout this process no data will be discarded (exceptany spectra where detector saturation was detected)—the total detectorcounts in each channel will be accumulated as well as the total exposuretime so that, when the loop exits, the accumulated spectrum can beconverted to the counts per second standard. Preferably, the usercontrols this data-collection process by setting both a S/N target and amaximum total exposure time.

Fifth, since the minerals with the weakest Raman signals tend to be darkand/or metallic, the auto-exposure routine has, in some embodiments, aone-click “Dark Mineral” option that begins at a higher exposure time(e.g., 20 seconds).

Sixth, one of the reasons the auto-exposure routine preferably repeatsat least once is to enable laser-damage detection. One of the indicatorsof specimen damage by the laser is that S/N of repeated exposuressteadily decrease. In preferred embodiments, the auto-exposure routinealerts the user if specimen damage is detected.

Finally, the blank spectrum is preferably subtracted from all samplespectra collected to determine the true sample spectra.

The spectrum processing and mineral identification software preferablyincludes algorithms to perform (1) removal of both CRE peaks andcontinuum background; (2) normalization and square-root squashing; (3)spectrum identification-scoring against a database of reference spectrausing an algorithm that both emphasizes coincidence of peaks andpenalizes for missing peaks; and (4) sorting of the identificationscores providing the user with a list of the top matches, which ispreferably organized in descending order from best match downwards.

The primary objective of spectrum processing for a PRMI system ismineral identification. In light of the variability of relative peakintensity in the Raman spectra of minerals, there is a need, whencomparing the “unknown” spectrum to reference spectra, to place highestpriority on clearly identifying cases where both spectra have peaks inthe same places and both spectra are devoid of peaks in the same places,and to place lower priority on measuring whether there is similarity inthe pattern of relative peak height.

In the following description of spectrum match-scoring systems the twospectra being compared are A and B where each consists of a sequence ofnumber pairs (Raman-shift, intensity) and where each contains intensityvalues for the same sequence of Raman-shift values. Based on thiscontext a_(i) and b_(i) are the intensities of each spectrum at thei^(th) value of Raman shift. The sums indicate summation over all valuesof i and computer-language syntax is used to express the equations used.

One popular match-scoring method is the well-established cosinesimilarity formula which consists of:Match=sum(a _(i) *b _(i))/(sqrt(sum(a _(i){circumflex over( )}2)*sqrt(sum(b _(i){circumflex over ( )}2))

However, the drawback of the cosine-similarity formula is that it adds asignificant positive contribution to the total identification score whenone of the compared spectra has a strong peak and the second spectra hasno peak at all in the same region. When judging whether the two spectraare from the same substance/phase such a missing peak should constitutea vote against the match not vote for it.

The present invention offers an improved match-scoring system, whichwill be referred to as an identification-scoring system containing twoterms: the coincident peak term and the missing peak penalty term.

The coincident peak term corresponds to the entirety of traditionalmatch-scoring formulae, however one worthwhile example would be simply:(a _(i) +b _(i))/2

This term will always be positive when both spectra have a peak in thesame region, but will have the highest value when both peaks havesimilar intensities.

One worthwhile example of a missing-peak penalty term would be:−V*abs(a _(i) −b _(i))*(1−min(a _(i) b _(i))){circumflex over ( )}Uwhere V is an adjustable parameter controlling the overall magnitude ofthe penalty term. While it is important to subtract from (penalize) theidentification score in the missing peak scenario, it is also importantfor this penalty to quickly disappear in cases where the lower intensityspectrum has even a weak peak in the same region. Here, U is anadjustable parameter controlling how fast the penalty term disappears.

The full identification-score formula is then:IDscore=sum((a _(i) +b _(i))/2−V*abs(a _(i) −b _(i))*(1−min(a _(i) ,b_(i))){circumflex over ( )}U)

FIG. 6 is a chart showing the behavior of the identification-scoreformula when comparing the Raman spectra of Nepheline and Quartz.

Preferably, the PRMI optimized spectrum processing software consists ofthe following:

First, the input spectrum to be matched/identified is prepared formatching as follows.

While basic pre-processing to remove CRE and continuum background iscommonly performed by the spectrometer-data-collection software, theoption to perform these pre-processing steps here is preferablyavailable.

The data is reset to a standard range and spacing of Raman-shift values,e.g., whole integer wavenumber values covering the range of thespectrometer (i.e., 100 to 1400 wavenumbers).

The reset spectrum is normalized to its highest intensity value and thenevery (normalized) intensity is replaced by its square root.

Second, the algorithm loops through comparing the prepared intensityarray to every spectrum in a reference database by calculating anidentification-score using the formula specified above.

Third, the identification scores are sorted and a list of the referencespectra (by phase name) is presented to the user sorted with the highestscoring first.

A display or output of one or more of: (a) a name and/or chemicalcomposition of one or more identified minerals; (b) the true Ramanspectrum data; and (c) the one or more reference spectrums; areprovided.

A prerequisite to practical use of the software is accumulation of adatabase of reference spectra and preparing them with identicalbackground removal, spectrum resetting, normalization, and square-rootsquashing.

Some embodiments of the invention also include use of a software systemfor removing noise from reference spectra. Preferably, the noise-removalsoftware is applied to the reference spectra before application of thepreviously described spectrum processing and identification software.Traditionally, many methods have been employed for noise reduction inspectrometry from a simple moving average to complex multi-stepfiltering algorithms. Their limitations include (1) they are ultimatelynoise reduction systems and not noise removal systems, and (2) they donot recognize the inherent regular shape of sample-signal peaks likeRaman peaks. The answer to this limitation is to model the spectrainstead of filtering them. While available software programs can be usedto fit mathematical peak models to the spectral data, considerablemethodology is employed in order to automatically model spectra havingany number of peaks and a wide variety of S/N characteristics.Accordingly, the peak-modeling software employed must be programmablevia some scripting language. Preferably, the methodology involves thefollowing:

First, the input spectra must have already gone through CRE removal andcontinuum background removal pre-processing, as described above.

Second, noise outliers are preferably removed. Noisy spectra have beenfound to contain occasional single-channel noise outliers that interferewith the success of the spectrum modeling. Preferably, a five-pointmoving average smoothing performs this function. While this proceduretends to slightly broaden and reduce the relative intensity of some ofthe exceptionally sharp and narrow primary peaks of minerals likecarbonates and sulfates, it actually improves pattern-match success forthese minerals by reducing sensitivity to spectrometer calibration.

Third, each spectra is normalized to its strongest peak (highestintensity value).

Fourth, since discrimination needs to be made between discernablespectroscopic peaks and high points in the noise, an estimate of theroot mean square (“RMS”) noise of the spectrum must be calculated. Anexample of how to accomplish this before the position of spectral peakshas been established is to (a) fit a series of spaced data points (e.g.every 5th data point) to a spline curve over a sub-region of thespectrum, and (b) calculate the RMS of the residuals between all of thespectral data points over the same sub-region and this spline curve.

Fifth, the first “curve” to be added to the model is a straight line 60,as depicted in FIG. 7a . This is necessary for baseline adjustment.While the true baseline of an instrumental spectrum would have anintensity of zero and instrumental noise would scatter on either side ofzero, most continuum background removal algorithms leave all data pointsgreater than zero—displaced from a true baseline.

Sixth, an iterative loop is entered that repeatedly performs thefollowing functions: (a) identifies a potential peak position, (b)creates a new model peak at this position, (c) fits the entireaccumulated spectrum model to the spectrum data by adjusting all peaksize/shape parameters, (d) calculates the intensity residuals betweenthe new model and each data point, (e) identifies the next potentialpeak position, and (f) compares the estimated height of this next-peakto a minimum peak-size limit based on the RMS noise level of thespectrum. When the potential next-peak has an estimated height smallerthan this limit, the iterative loop is exited. FIG. 7a also shows thestarting spectrum data 61, as well as the modelled peaks 62.

Since most peak-finding algorithms have trouble resolving significantlyoverlapping peaks, which is a very common feature of Raman spectra, aneffective method for identifying the next potential peak is topreferably calculate the spectral position of the maximum value of themodel-data residuals.

Since Raman peaks are known to have shapes that are a combination ofGaussian and Lorentzian shapes, it is preferred that each peak addeduses a peak-shape formula that variably combines these two, such as thePseudo-Voigt formula.

It is preferred that the peak size/shape parameters be constrained towithin a range of realistic values. For example, parameters like fullwidth at half maximum (“FWHM”) should not be allowed to have negativevalues.

The minimum peak-size limit that is used to control when the iterativeloop is exited is based on the need to distinguish the smallest peakthat is likely to represent spectroscopic data from a random high in theinstrumental noise. Through testing on a variety of example spectra, theinventor found that the minimum-peak-size limit preferably needs to beabout three times the RMS noise.

The spectrum model being created and refined via the iterative loop iscapable, if not likely, to become a more accurate model of the spectrumthan the spline-curve method employed for the initial RMS noiseestimation. Accordingly, each time a new model is created and fitted inthe iterative loop, the root-mean-square of the model residuals ispreferably compared to the initial RMS estimate and, if found to belower, adopted as the new RMS noise estimate.

Seventh, the initial straight-line function is preferably subtractedfrom the model to bring its baseline down to zero.

Eighth, having a mathematical model for the spectrum, the modeledversion of the spectrum is preferably output as a series of data points(Raman-shift, intensity) calculated using the model. In someembodiments, this is done using a specific desired data spacing (e.g.,whole wavenumber values) and a specific range of Raman-shift values.Both the modeled spectrum and a square-root squashed version of themodel can be output at the same time.

In many cases this procedure produces one or more small “peaks” whoserole in the model is to fill in where the Pseudo-Voigt shape does notadequately describe the real Raman peaks. While these smallshape-filling peaks do not themselves represent real Raman “modes” ofthe material the spectrum was collected from, they do not detract fromthe validity of the spectrum model created by this method.

The resulting modeled spectrum is a smoothly varying curve with norandom intensity variations, as seen in FIG. 7b . Although thisprocedure is too computationally slow for real-time data processing, itcan be applied once, in advance, to all of the reference spectra beforedeploying the reference-spectra database. The resulting noise-freereference database improves the performance of spectrum-match (speciesidentification) software, particularly when the instrumental spectrum ofthe unknown is significantly noisy (i.e., has moderate to low S/N).

In one embodiment of the method of identifying minerals, the steps ofthe method provide for illuminating a sample holder area having nosample therein for a blank illumination duration with monochromaticlight having a wavelength; collecting a blank scattered light resultingfrom the blank illumination duration using at least one Ramanspectrometer detector; executing software in a digital computer todetermine a blank spectrum data corresponding to the blank scatteredlight dividing all spectrometer intensities by the exposure durationtime to record all intensities as counts per second; illuminating amineral sample in the sample holder area for a first illuminationduration with the same monochromatic light and collecting a firstscattered light resulting from the first illumination duration using theat least one Raman spectrometer detector; executing software in adigital computer to determine a first Raman spectrum data correspondingto the first scattered light dividing all spectrometer intensities bythe exposure duration time to record all intensities as counts persecond; and executing software in a digital computer to determine a trueRaman spectrum data determined by a difference between the first Ramanspectrum data and the blank spectrum data. Desirably, the methodincludes additional steps of illuminating the mineral sample for asecond or more illumination duration(s) with the same monochromaticlight and collecting second or more scattered light resulting from thesecond illumination duration(s) using the at least one Ramanspectrometer detector; executing software in a digital computer todetermine second or more Raman spectrum data corresponding to the secondor more scattered light dividing all spectrometer intensities by theexposure duration time to record all intensities as counts per second;executing software in a digital computer to determine an aggregated oraverage Raman spectrum data corresponding to the first scattered lightand the second or more scattered light; and executing software in adigital computer to determine the true Raman spectrum data determined bya difference between the aggregated or average Raman spectrum data andthe blank spectrum data. Preferably, the monochromatic light has awavelength in the range of about 400 nm to about 425 nm. Preferably theRaman spectrometer detector is adapted to detect a Raman-shift range ofabout 100 cm⁻¹ to about 1400 cm⁻¹.

The true Raman spectrum data is compared to one or more referencespectrums to determine if the true Raman spectrum data corresponds toone or more reference spectrums by a software process executed on adigital computer. Preferably, the reference spectrums comprisemathematical models of Raman spectrums of different minerals and/ornoise-free reference spectrum data sets generated by mathematical modelsof Raman spectrums of minerals.

One or more of: (a) a name and/or chemical composition of one or moreminerals identified by the one or more reference spectrums correspondingto the true Raman spectrum data; (b) the true Raman spectrum data; and(c) the one or more reference spectrums corresponding to the true Ramanspectrum data are output by display on a screen, printout, and/or savingto a data file when reference spectrum(s) corresponding to the trueRaman spectrum data are identified.

The determination of whether the one or more reference spectrumscorresponding to the true Raman spectrum data is preferably provided bycalculating an identification score for each reference spectrum relativeto the true Raman spectrum data using a formula that includes both acoincident-peak term and a missing-peak-penalty term. In one embodiment,the identification score is determined by the formula:IDscore=sum((a _(i) +b _(i))/2−V*abs(a _(i) −b _(i))*(1−min(a _(i) ,b_(i))){circumflex over ( )}U),wherein a_(i) and b_(i) are the intensities of the true first spectrumdata and the reference spectrum, respectively, at the i^(th) value ofRaman shift, and V and U are user-selectable parameters.

The calculated identification scores can be sorted in descending order;and an output may be presented that includes one or more of: (a) a nameand/or chemical composition of one or more minerals identified by one ormore reference spectrums corresponding to the true Raman spectrum data;(b) the true Raman spectrum data; (c) the one or more referencespectrums corresponding to the true Raman spectrum data; and (d) thecalculated identification score of the one or more reference spectrumscorresponding to the true Raman spectrum data.

The method desirably also provides that if collected spectrum dataindicates saturation of a detector used to receive the scattered light,then that spectrum data is not used in calculating the true Ramanspectrum data, and preferably, the method automatically runs anadditional test cycle where the time period of the additionalillumination duration is less than the illumination durations thatcaused saturation in the detector.

Similarly, if a minimum if a signal-to-noise ratio minimum is not metand the Raman spectrum data does not indicate the presence of at leastone peak, then the method automatically runs an additional test cyclewhere the time period of the additional illumination duration is greaterthan the illumination durations of the prior test cycles.

Although the invention has been described with reference to a particulararrangement of parts, features, and the like, these are not intended toexhaust all possible arrangements or features. Indeed, many othermodifications and variations will be ascertainable to those of skill inthe art.

What is claimed is:
 1. A method of determining a Raman spectrum of amineral, comprising: illuminating a sample holder area having no sampletherein for a blank illumination duration with monochromatic lighthaving a wavelength; collecting a blank scattered light resulting fromthe blank illumination duration using at least one Raman spectrometerdetector; executing software in a digital computer to determine a blankspectrum data corresponding to the blank scattered light; illuminating amineral sample in the sample holder area for a first illuminationduration with monochromatic light having a wavelength which is the sameas the wavelength of the monochromatic light used to illuminate thesample holder area having no sample therein for the blank illuminationduration; collecting a first scattered light resulting from the firstillumination duration using the at least one Raman spectrometerdetector; executing software in a digital computer to determine a firstRaman spectrum data corresponding to the first scattered light; andexecuting software in a digital computer to determine a true Ramanspectrum data determined by a difference between the first Ramanspectrum data and the blank spectrum data.
 2. The method of claim 1,further comprising the following steps: illuminating the mineral samplefor a second or more illumination duration(s) with monochromatic lighthaving a wavelength which is the same as the wavelength of themonochromatic light used to illuminate the sample holder area having nosample therein for the blank illumination duration; collecting second ormore scattered light resulting from the second illumination duration(s)using the at least one Raman spectrometer detector; executing softwarein a digital computer to determine second or more Raman spectrum datacorresponding to the second or more scattered light; executing softwarein a digital computer to determine an aggregated Raman spectrum datacorresponding to the first scattered light and the second or morescattered light; and executing software in a digital computer todetermine the true Raman spectrum data determined by a differencebetween the aggregated Raman spectrum data and the blank spectrum data.3. The method of claim 2, further comprising the following steps: whenthe first spectrum data or second or more spectrum data indicatessaturation of a detector used to receive the scattered light, then:illuminating the mineral sample for a third illumination duration withmonochromatic light, wherein the time period of the third illuminationduration is less than the first illumination duration or secondillumination duration; collecting third scattered light resulting fromthe third illumination duration using the at least one Ramanspectrometer detector; and executing software in a digital computer todetermine an aggregated Raman spectrum data from the collected scatteredlight for all illumination durations, but omitting the spectrum dataindicating saturation of a detector from the determination of theaggregated Raman spectrum data.
 4. The method of claim 2, furthercomprising the following steps: when the first spectrum data or secondor more spectrum data does not indicate the presence of at least onepeak, then: illuminating the mineral sample for a fourth illuminationduration with monochromatic light, wherein the time period of the fourthillumination duration is greater than the first or second or moreillumination durations; collecting fourth scattered light resulting fromthe fourth illumination duration using the at least one Ramanspectrometer detector; and executing software in a digital computer todetermine the aggregated Raman spectrum data from the collectedscattered light for all illumination durations.
 5. The method of claim2, wherein the step of executing software in a digital computer furthercomprises: determining a signal-to-noise ratio of the aggregated Ramanspectrum data; comparing this signal-to-noise ratio to a signal-to-noiseratio minimum; and when the signal-to-noise ratio is below thesignal-to-noise ratio minimum, collecting a fifth or more scatteredlight resulting from the fifth or more illumination duration using theat least one Raman spectrometer detector; determining the aggregatedRaman spectrum data and determining the signal-to-noise ratio of theaggregated Raman spectrum data until it is equal to or greater to thesignal-to-noise ratio minimum.
 6. The method of claim 1, wherein themonochromatic light has a wavelength in the range of about 400 nm toabout 425 nm.
 7. The method of claim 1, wherein the at least one Ramanspectrometer detector is adapted to detect a Raman-shift range of about100 cm⁻¹ to about 1400 cm⁻¹.
 8. A method of determining a Raman spectrumof a mineral, comprising: receiving an initial exposure time, asignal-to-noise ratio target and a maximum exposure time; illuminating asample holder area having no sample therein for a blank illuminationduration equal to the maximum exposure time with monochromatic lighthaving a wavelength; collecting a blank scattered light resulting fromthe blank illumination duration using at least one Raman spectrometerdetector; executing software in a digital computer to determine a blankspectrum data corresponding to the blank scattered light dividing allspectrometer intensities by the exposure duration time to record allintensities as counts per second; storing the blank spectrum data in amemory; illuminating a mineral sample in the sample holder area for afirst illumination duration equal to the initial exposure time withmonochromatic light having a wavelength which is the same as thewavelength of the monochromatic light used to illuminate the sampleholder area having no sample therein for the blank illuminationduration; collecting a first scattered light resulting from the firstillumination duration using the at least one Raman spectrometerdetector; executing software in a digital computer to determine a firstRaman spectrum data corresponding to the first scattered light dividingall spectrometer intensities by the exposure duration time to record allintensities as counts per second; and executing software in a digitalcomputer to determine a true Raman spectrum data using the firstspectrum data adjusted by the blank spectrum data by subtracting thestored blank spectrum.
 9. The method of claim 8, further comprising thefollowing steps: illuminating the mineral sample for a second or moreillumination duration(s) with monochromatic light having a wavelengthwhich is the same as the wavelength of the monochromatic light used toilluminate the sample holder area having no sample therein for the blankillumination duration; collecting second or more scattered lightresulting from the second illumination duration(s) using the at leastone Raman spectrometer detector; executing software in a digitalcomputer to determine second or more Raman spectrum data correspondingto the second or more scattered light recording all spectrometerintensities in counts per second units; executing software in a digitalcomputer to determine an average Raman spectrum of the mineral sample bycalculating the average of the recorded intensities for the first,second, and any additional illumination durations; and executingsoftware in a digital computer to determine a true Raman spectrum datausing the average of the first spectrum data and the second or morespectrum data adjusted by the blank spectrum data by subtracting thestored blank spectrum.
 10. The method of claim 9, further comprising thefollowing steps: when the first spectrum data or second or more spectrumdata indicates saturation of a detector used to receive the scatteredlight, then: illuminating the mineral sample for a third illuminationduration with monochromatic light, wherein the time period of the thirdillumination duration is less than the first illumination duration orsecond illumination duration; collecting third scattered light resultingfrom the third illumination duration using the at least one Ramanspectrometer detector; executing software in a digital computer todetermine the third Raman spectrum data corresponding to the thirdscattered light recording all spectrometer intensities in counts persecond units; and executing software in a digital computer to determinethe average Raman spectrum of the mineral sample by calculating theaverage of the recorded intensities for the first, second, and anyadditional illumination durations, but omitting spectrum data indicatingsaturation of a detector from the determination of the average Ramanspectrum data.
 11. The method of claim 9, further comprising thefollowing steps: when the signal-to-noise ratio target is not met and ifthe first spectrum data or second or more spectrum data does notindicate the presence of at least one peak, then: illuminating themineral sample for a fourth illumination duration with monochromaticlight, wherein the time period of the fourth illumination duration isgreater than the first or second or more illumination durations;collecting additional scattered light resulting from the additionalillumination duration using the at least one Raman spectrometerdetector; executing software in a digital computer to determine theadditional Raman spectrum data corresponding to the additional scatteredlight recording all spectrometer intensities in counts per second units;and executing software in a digital computer to determine the averageRaman spectrum of the mineral sample by calculating the average of therecorded intensities for the first, second, and any additionalillumination durations.
 12. The method of claim 9, further comprisingthe following steps: determining signal-to-noise ratio of the average ofthe first spectrum data and the second or more spectrum data; comparingthis signal-to-noise ratio to the signal-to-noise ratio target; when thesignal-to-noise ratio is below the signal-to-noise ratio target,collecting a fifth or more scattered light resulting from the fifth ormore illumination duration using the at least one Raman spectrometerdetector; recording all spectrometer intensities in counts per secondunits; and determining the average Raman spectrum data and determiningthe signal-to-noise ratio of the average Raman spectrum data until it isequal to or greater to the signal-to-noise ratio target.
 13. The methodof claim 8, further comprising: illuminating another mineral sample inthe sample holder area for a another illumination duration withmonochromatic light having a wavelength which is the same as thewavelength of the monochromatic light used to illuminate the sampleholder area having no sample therein for the blank illuminationduration; collecting another scattered light resulting from the anotherillumination duration using the at least one Raman spectrometerdetector; executing software in a digital computer to determine anotherspectrum data corresponding to the another scattered light; andexecuting software in a digital computer to determine a true Ramanspectrum data using the another spectrum data adjusted by the blankspectrum data by subtracting the stored blank spectrum.
 14. The methodof claim 8, wherein the monochromatic light has a wavelength in therange of about 400 nm to about 425 nm.
 15. The method of claim 8,wherein the at least one Raman spectrometer detector is adapted todetect a Raman-shift range of about 100 cm⁻¹ to about 1400 cm⁻¹.